Influence of land use activities on predicted soil loss in a semi-arid river basin
Bibliographic record
Abstract
ABSTRACT Soil loss due to land transformations is a serious issue confronting the globe nowadays. The research's main focus was to predict future land use and land cover (LULC) and quantify soil loss, which is exacerbated by excessive rainfall following uneven topography, intensive agriculture, and a lack of adequate watershed management strategies. The Landsat satellite data were classified using maximum likelihood algorithm, and future LULC (2030 and 2040) was quantified using TerrSet Land Change Modeler through Markov Chain Model. In addition, the RUSLE was applied to estimate soil loss based on LULC data from various years, and the results were evaluated using sediment observation data. In this research, the LS-factor has been quantified by employing open-source digital elevation models (DEMs) (SRTM, ASTER, MERIT, AW3D30, NASADEM, CARTOSAT, and TanDEM-X). Furthermore, hypsometry analysis was carried out to assess erosion vulnerability at the sub-watershed. The results showed that SRTM 30-m DEM-based soil loss corresponds to observation. Moreover, soil loss is estimated at 16.55 t/ha/year for 2015, whereas future soil loss may be reduced to 14.51 and 14.46 t/ha/year in 2030 and 2040, respectively.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".